Abdul Razik1, Ankur Goyal1, Raju Sharma1, Devasenathipathy Kandasamy1, Amlesh Seth2, Prasenjit Das3, Balaji Ganeshan4. 1. Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India. 2. Departments of Urology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India. 3. Departments of Pathology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India. 4. Institute of Nuclear Medicine, University College London Hospital NHS Trust, London, United Kingdom.
Abstract
OBJECTIVES: To assess the utility of magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma. METHODS: After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence. RESULTS: Several texture parameters on MRI showed high-class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000. CONCLUSIONS: MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma. ADVANCES IN KNOWLEDGE: There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard.
OBJECTIVES: To assess the utility of magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma. METHODS: After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence. RESULTS: Several texture parameters on MRI showed high-class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000. CONCLUSIONS: MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma. ADVANCES IN KNOWLEDGE: There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard.
Authors: HeiShun Yu; Jonathan Scalera; Maria Khalid; Anne-Sophie Touret; Nicolas Bloch; Baojun Li; Muhammad M Qureshi; Jorge A Soto; Stephan W Anderson Journal: Abdom Radiol (NY) Date: 2017-10
Authors: Jonathan R Young; Daniel Margolis; Steven Sauk; Allan J Pantuck; James Sayre; Steven S Raman Journal: Radiology Date: 2013-02-04 Impact factor: 11.105